About the Project

Agora Energiewende aims to model the Kazakh power system, incorporating a substantial increase in variable generation, such as solar and wind, surpassing the current official mid-term policy goal of 15% of all renewable energy sources (RES) in generation by 2030. This endeavor has received support from Open Energy Transition on the modeling side.

Development status: Active and Stable

pypsa-kz-data

Extra data for the Kazakhstan model that will be used as input for PyPSA-Earth. Repo design oriented on: https://github.com/pypsa-meets-earth/pypsa-zm-data

Data

Contains openly available data for Kazakhstan

Demand data

Monthly electricity demand data with monthly aggregation provided by Kazakhstan operator of the electric energy and power market

kz_demand_validation.csv (demand_valid_clean.R contains for details on how data were extracted and a simple vizualization; needs to work any R terminal + installation of two libraries)

Model Execution

Setting up the general repositories

The provided workflow builds on PyPSA-Earth. Therefore, first, the PyPSA-Earth repository must be forked and the fork should then be cloned. A fork can be created by navigating to the PyPSA-Earth website. By clicking on the fork-symbol in the upper right corner, a fork is created and linked to the specific user.

Next, we also need to fork the pypsa-kz-data repository. A fork can be created by navigating to the pypsa-kz-data website and clicking the fork symbol in the upper right corner.

In order to clone both forks to the correct locations on a local machine, the following commands can be used using the local machines shell:

git clone https://github.com/<user-name>/pypsa-earth

<user-name> must be replaced with the personal github-username.

After that, one must change to the freshly created pypsa-earth repository.

cd pypsa-earth/

and repeat the cloning, this time for the pypsa-kz-data repository.

git clone https://github.com/<user-name>/pypsa-kz-data

Again, <user-name> must be replaced with the personal github-username.

In order to install the pypsa-earth environment, instructions are provided in the pypsa-earth documentation, see Install dependencies and Python dependencies. After installing the environment, activate it using

conda activate pypsa-earth

Modeling adaptations for KZ study

To adapt the overall workflow for kz, only two further changes are necessary.

Firstly, open the Snakefile (in pypsa-earth/) and navigate to line 1057-1058, which should read

os.system("snakemake -j all solve_all_networks --rerun-incomplete")
os.system("snakemake -j1 make_statistics --force")

and replace these two lines with

os.system(f"snakemake -j1 networks/{wildcards.scenario_name}/base.nc")
os.system("cp pypsa-kz-data/data/custom_powerplants.csv data/custom_powerplants.csv")
os.system("snakemake -j1 solve_everything --rerun-incomplete")

Secondly, copy the default configuration file to the pypsa-earth folder using:

cp pypsa-kz-data/config.kz_default.yaml config.default.yaml

In case you already have a custom config file, make sure to replace it as well, using

cp pypsa-kz-data/config.kz_default.yaml config.yaml

You are now all set to run all scenarios!

Running KZ scenarios

To prepare running all scenarios, execute

snakemake -j1 prepare_kz_scenarios

Optionally, to save time for future runs, you can now set enable: retrieve_databundle: True in the config.yaml to False. If you already have build all cutouts for 2011, 2013 and 2018, you can also set enable: build_cutout: True to False. Finally, to run all scenarios, execute

snakemake -j1 run_all_scenarios

After all scenarios have executed successfully, all results are generated and locally saved in pypsa-earth/results/<scenario_folder>/networks/.

Potential errors

• A rule is killed. In this case, open the Snakefile in pypsa-earth or open kz.smk in pypsa-kz-data (depending on the rule which is killed), navigate to the rule that is being killed in the workflow and increase the memory assignment (for example, add a 0 at the end).

• The workflow runs into an error during the build_powerplants rule. In this case, try to repeat step 1. of the workflow using the command

cp pypsa-kz-data/data/custom_powerplants.csv data/custom_powerplants.csv

• Unusual error arising from either Snakemake or the Snakefile and proving to be challenging to comprehend: Inspect all indentation. Ensure there is no tab spacing; employ only spaces, i.e., . It is probable that the indentations before

os.system(f"snakemake -j1 networks/{wildcards.scenario_name}/base.nc")
os.system("cp pypsa-kz-data/data/custom_powerplants.csv data/custom_powerplants.csv")
os.system("snakemake -j1 solve_everything --rerun-incomplete")

are tabs instead of four spaces.

• Missing data/ folder or some relevant subfolders. This should normally be executed automatically when executing the rule prepare_kz_scenarios, however might be missing due to incorrect execution. The databundle can be also retrieved manually via:

snakemake -j 1 retrieve_databundle_light

• The rule retrieve_databundle_light always executes with an error. To avoid this, try setting enable: build_cutout: False to True.

Comes in handy

After all cutouts were generated (i.e. the three files asia-<year>-era5.nc exist in the folder pypsa-earth/cutouts/, where <year> is 2011, 2013, and 2018, navigate to pypsa-earth/pypsa-kz-data, open the default config file, navigate to line 36, which should read build_cutout: True, and set it to build_cutout: false. This will save you a lot of time when (re-)runnig scenarios. But remember to set it back to true in case one of the cutouts was deleted!

Acknowledgement

Code development and testing: Open Energy Transition

Model assumptions: Agora Energiewende